#' @include sim_class.R generics.R model_helper.R
NULL
#' Validity Checker for markov_sim Object
#'
#' @param object A markov_sim object
#' @return \code{TRUE} if the input sim object is valid, vector of error messages otherwise.
#' @keywords internal
check_valid_markov_sim <- function(object) {
errors <- character()
cluster_type_choices <- c("fixed", "quantile")
window_type_choices <- c("max", "avg")
if (length(object@window_size_for_reg) != 1) {
msg <- paste0("window_size_for_reg must be one of ", paste(window_type_choices, collapse = " "))
errors <- c(errors, msg)
}
if (length(object@window_type_for_reg) != 1 | is.na(object@window_type_for_reg) | all(object@window_type_for_reg != window_type_choices)) {
msg <- paste0("window_type_for_reg must be one of ", paste(window_type_choices, collapse = " "))
errors <- c(errors, msg)
}
if (any(is.na(object@state_num)) | any(object@state_num %% 1 != 0) | any(object@state_num <= 0)) {
msg <- paste0("state_num must only consist positive integers.")
errors <- c(errors, msg)
}
if (length(object@cluster_type) != 1 | is.na(object@cluster_type) | all(object@cluster_type != cluster_type_choices)) {
msg <- paste0("cluster_type must be one of ", paste(cluster_type_choices, collapse = " "), ".")
errors <- c(errors, msg)
}
if (length(errors) == 0) {
return(TRUE)
} else {
return(errors)
}
}
#' @rdname sim-class
#' @param state_num A numeric number that represents the number of states in Markov chain. Default value is \code{8}.
#' @param cluster_type A character that represents how each state is partitioned. It can only be either \code{"fixed"} for fixed partitioning from \code{0} to \code{100}, or \code{"quantile"} for dynamic partitioning from minimum value to maximum value using quantiles. Default value is \code{"fixed"}.
#' @export markov_sim
markov_sim <- setClass("markov_sim",
slots = list(window_size_for_reg = "numeric",
window_type_for_reg = "character",
state_num = "numeric",
cluster_type = "character"),
prototype = list(window_size_for_reg = NA_real_,
window_type_for_reg = "avg",
name = "MARKOV",
state_num = 8,
cluster_type = "fixed",
probability_function = find_state_based_cdf,
probability_expectation = find_expectation_state_based_dist,
probability_mean_shift = find_shifted_state_based_dist),
contains = "sim",
validity = check_valid_markov_sim)
#' @describeIn train_model Train Markov Model specific to markov_sim object.
setMethod("train_model",
signature(object = "markov_sim", train_x = "matrix", train_xreg = "NULL", trained_model = "list"),
function(object, train_x, train_xreg, trained_model) {
new_train_x <- convert_frequency_dataset_overlapping(train_x, object@window_size, object@response, keep.names = TRUE)
from_quantiles_x <- c(stats::quantile(new_train_x[-c((length(new_train_x) - object@window_size + 1):length(new_train_x))], probs = seq(to = 1, by = 1 / (object@state_num - 1), length.out = object@state_num - 1), names = FALSE), 100)
from_states_x <- sapply(new_train_x[-c((length(new_train_x) - object@window_size + 1):length(new_train_x))], find_state_num, object@cluster_type, object@state_num, from_quantiles_x)
to_states_x <- sapply(new_train_x[-c(1:object@window_size)], find_state_num, object@cluster_type, object@state_num, from_quantiles_x)
uncond_dist_x <- rep(0, object@state_num)
transition_x_x <- matrix(0, nrow = object@state_num, ncol = object@state_num)
for (i in 1:length(from_states_x)) {
from <- from_states_x[i]
to <- to_states_x[i]
transition_x_x[from, to] <- transition_x_x[from, to] + 1
uncond_dist_x[to] <- uncond_dist_x[to] + 1
}
for (r in 1:ncol(transition_x_x)) {
if (sum(transition_x_x[r,]) == 0) {
transition_x_x[r,] <- uncond_dist_x / sum(uncond_dist_x)
} else {
transition_x_x[r,] <- transition_x_x[r,] / sum(transition_x_x[r,])
}
}
trained_result <- list("transition_x_x" = transition_x_x, "quantiles_x" = from_quantiles_x, "train_x" = new_train_x)
return(trained_result)
})
#' @describeIn do_prediction Do prediction based on trained Markov Model.
setMethod("do_prediction",
signature(object = "markov_sim", trained_result = "list", predict_info = "data.frame", test_x = "matrix", test_xreg = "NULL"),
function(object, trained_result, predict_info, test_x, test_xreg) {
compute_pi_up <- function(prob, to_states, quantiles=NULL) {
current_state <- 1
current_prob <- 0
while (current_state <= length(to_states)) {
current_prob <- current_prob + to_states[current_state]
if (current_prob < prob) {
current_state <- current_state + 1
} else {
break
}
}
if (is.null(quantiles)) {
pi_up <- current_state * (100 / length(to_states))
} else {
pi_up <- quantiles[current_state]
}
return(pi_up)
}
if (nrow(predict_info) == 0) {
from <- find_state_num(trained_result$train_x[length(trained_result$train_x)], object@cluster_type, object@state_num, trained_result$quantiles_x)
} else {
from <- find_state_num(predict_info$actual[length(predict_info$actual)], object@cluster_type, object@state_num, trained_result$quantiles_x)
}
final_transition <- trained_result$transition_x_x
to_states <- final_transition[from,]
if (object@cluster_type == "fixed") {
pi_up <- as.data.frame(matrix(sapply(sort(1 - object@cut_off_prob), function(i) {
compute_pi_up(i, to_states, NULL)
}), nrow = 1, ncol = length(object@cut_off_prob)))
colnames(pi_up) <- paste0("Quantile_", sort(1 - object@cut_off_prob))
predicted_params <- as.data.frame(matrix(to_states, nrow = 1, ncol = object@state_num))
colnames(predicted_params) <- paste0("prob_dist.", 1:length(to_states))
} else {
pi_up <- as.data.frame(matrix(sapply(sort(1 - object@cut_off_prob), function(i) {
compute_pi_up(i, to_states, trained_result$quantiles_x)
}), nrow = 1, ncol = length(object@cut_off_prob)))
colnames(pi_up) <- paste0("Quantile_", sort(1 - object@cut_off_prob))
predicted_params <- as.data.frame(matrix(c(to_states, trained_result$quantiles_x), nrow = 1, ncol = 2 * object@state_num))
colnames(predicted_params) <- c(paste0("prob_dist.", 1:length(to_states)), paste0("quantiles.", 1:length(trained_result$quantiles_x)))
}
if (object@extrap_step > 1) {
for (i in 1:(object@extrap_step - 1)) {
final_transition <- final_transition %*% final_transition
to_states <- final_transition[from,]
if (object@cluster_type == "fixed") {
pi_up <- rbind(pi_up, sapply(sort(1 - object@cut_off_prob), function(i) {
compute_pi_up(i, to_states, NULL)
}))
predicted_params <- rbind(predicted_params, to_states)
} else {
pi_up <- rbind(pi_up, sapply(sort(1 - object@cut_off_prob), function(i) {
compute_pi_up(i, to_states, trained_result$quantiles_x)
}))
predicted_params <- rbind(predicted_params, c(to_states, trained_result$quantiles_x))
}
}
}
predicted_params[,"type"] <- object@cluster_type
expected <- data.frame("expected" = sapply(1:object@extrap_step, function(i) {
if (object@cluster_type == "fixed") {
find_expectation_state_based_dist(predicted_params[i, grep("prob_dist.", colnames(predicted_params))])
} else {
find_expectation_state_based_dist(predicted_params[i,grep("prob_dist.", colnames(predicted_params))],
predicted_params[i,grep("quantiles.", colnames(predicted_params))])
}
}))
return(list("predicted_quantiles" = cbind(expected, pi_up), "predicted_params" = predicted_params))
})
#' @describeIn train_model Train Markov Model specific to markov_sim object.
setMethod("train_model",
signature(object = "markov_sim", train_x = "matrix", train_xreg = "matrix", trained_model = "list"),
function(object, train_x, train_xreg, trained_model) {
new_train_x <- convert_frequency_dataset_overlapping(train_x[(max(object@window_size, object@window_size_for_reg) + 1):nrow(train_x), 1], object@window_size, object@response, keep.names = TRUE)
new_train_xreg <- convert_frequency_dataset_overlapping(train_xreg[(max(object@window_size - object@window_size_for_reg, 0) + 1):(nrow(train_x) - object@window_size), 1], object@window_size_for_reg, object@window_type_for_reg, keep.names = TRUE)
from_quantiles_x <- c(stats::quantile(new_train_x[-c((length(new_train_x) - object@window_size + 1):length(new_train_x))], probs = seq(to = 1, by = 1 / (object@state_num - 1), length.out = object@state_num - 1), names = FALSE), 100)
from_states_x <- sapply(new_train_x[-c((length(new_train_x) - object@window_size + 1):length(new_train_x))], find_state_num, object@cluster_type, object@state_num, from_quantiles_x)
to_states_x <- sapply(new_train_x[-c(1:object@window_size)], find_state_num, object@cluster_type, object@state_num, from_quantiles_x)
uncond_dist_x <- rep(0, object@state_num)
transition_x_x <- matrix(0, nrow = object@state_num, ncol = object@state_num)
for (i in 1:length(from_states_x)) {
from <- from_states_x[i]
to <- to_states_x[i]
transition_x_x[from, to] <- transition_x_x[from, to] + 1
uncond_dist_x[to] <- uncond_dist_x[to] + 1
}
for (r in 1:ncol(transition_x_x)) {
if (sum(transition_x_x[r,]) == 0) {
transition_x_x[r,] <- uncond_dist_x / sum(uncond_dist_x)
} else {
transition_x_x[r,] <- transition_x_x[r,] / sum(transition_x_x[r,])
}
}
from_quantiles_xreg <- c(stats::quantile(new_train_xreg, probs = seq(to = 1, by = 1 / (object@state_num - 1), length.out = object@state_num - 1), names = FALSE), 100)
from_states_xreg <- sapply(new_train_xreg, find_state_num, object@cluster_type, object@state_num, from_quantiles_xreg)
to_states_x <- sapply(new_train_x, find_state_num, object@cluster_type, object@state_num, from_quantiles_x)
transition_xreg_x <- matrix(0, nrow = object@state_num, ncol = object@state_num)
for (i in 1:length(from_states_xreg)) {
from <- from_states_xreg[i]
to <- to_states_x[i]
transition_xreg_x[from, to] <- transition_xreg_x[from, to] + 1
uncond_dist_x[to] <- uncond_dist_x[to] + 1
}
for (r in 1:ncol(transition_xreg_x)) {
if (sum(transition_xreg_x[r,]) == 0) {
transition_xreg_x[r,] <- uncond_dist_x / sum(uncond_dist_x)
} else {
transition_xreg_x[r,] <- transition_xreg_x[r,] / sum(transition_xreg_x[r,])
}
}
trained_result <- list("transition_x_x" = transition_x_x, "transition_xreg_x" = transition_xreg_x, "quantiles_x" = from_quantiles_x, "quantiles_xreg" = from_quantiles_xreg, "train_x" = new_train_x, "train_xreg" = new_train_xreg, "orig_x" = train_x, "orig_xreg" = train_xreg)
return(trained_result)
})
#' @describeIn do_prediction Do prediction based on trained Markov Model.
setMethod("do_prediction",
signature(object = "markov_sim", trained_result = "list", predict_info = "data.frame", test_x = "matrix", test_xreg = "matrix"),
function(object, trained_result, predict_info, test_x, test_xreg) {
compute_pi_up <- function(prob, to_states, quantiles=NULL) {
current_state <- 1
current_prob <- 0
while (current_state <= length(to_states)) {
current_prob <- current_prob + to_states[current_state]
if (current_prob < prob) {
current_state <- current_state + 1
} else {
break
}
}
if (is.null(quantiles)) {
pi_up <- current_state * (100 / length(to_states))
} else {
pi_up <- quantiles[current_state]
}
return(pi_up)
}
if (nrow(predict_info) == 0) {
from <- find_state_num(convert_frequency_dataset(
trained_result$orig_xreg[(length(trained_result$orig_xreg) - object@window_size_for_reg + 1):length(trained_result$orig_xreg),1],
object@window_size_for_reg,
object@window_type_for_reg
), object@cluster_type, object@state_num, trained_result$quantiles_xreg)
} else {
new_xreg <- rbind(trained_result$orig_xreg, test_xreg)
new_xreg <- new_xreg[(length(trained_result$orig_xreg) - object@window_size_for_reg + 1):length(trained_result$orig_xreg),1]
from <- find_state_num(convert_frequency_dataset(new_xreg, object@window_size_for_reg, object@window_type_for_reg),
object@cluster_type,
object@state_num,
trained_result$quantiles_xreg)
}
final_transition <- trained_result$transition_xreg_x
to_states <- final_transition[from,]
if (object@cluster_type == "fixed") {
pi_up <- as.data.frame(matrix(sapply(sort(1 - object@cut_off_prob), function(i) {
compute_pi_up(i, to_states, NULL)
}), nrow = 1, ncol = length(object@cut_off_prob)))
colnames(pi_up) <- paste0("Quantile_", sort(1 - object@cut_off_prob))
predicted_params <- as.data.frame(matrix(to_states, nrow = 1, ncol = object@state_num))
colnames(predicted_params) <- paste0("prob_dist.", 1:length(to_states))
} else {
pi_up <- as.data.frame(matrix(sapply(sort(1 - object@cut_off_prob), function(i) {
compute_pi_up(i, to_states, trained_result$quantiles_x)
}), nrow = 1, ncol = length(object@cut_off_prob)))
colnames(pi_up) <- paste0("Quantile_", sort(1 - object@cut_off_prob))
predicted_params <- as.data.frame(matrix(c(to_states, trained_result$quantiles_x), nrow = 1, ncol = 2 * object@state_num))
colnames(predicted_params) <- c(paste0("prob_dist.", 1:length(to_states)), paste0("quantiles.", 1:length(trained_result$quantiles_x)))
}
if (object@extrap_step > 1) {
for (i in 1:(object@extrap_step - 1)) {
final_transition <- final_transition %*% trained_result$transition_x_x
to_states <- final_transition[from,]
if (object@cluster_type == "fixed") {
pi_up <- rbind(pi_up, sapply(sort(1 - object@cut_off_prob), function(i) {
compute_pi_up(i, to_states, NULL)
}))
predicted_params <- rbind(predicted_params, to_states)
} else {
pi_up <- rbind(pi_up, sapply(sort(1 - object@cut_off_prob), function(i) {
compute_pi_up(i, to_states, trained_result$quantiles_x)
}))
predicted_params <- rbind(predicted_params, c(to_states, trained_result$quantiles_x))
}
}
}
predicted_params[,"type"] <- object@cluster_type
expected <- data.frame("expected" = sapply(1:object@extrap_step, function(i) {
if (object@cluster_type == "fixed") {
find_expectation_state_based_dist(predicted_params[i, grep("prob_dist.", colnames(predicted_params))])
} else {
find_expectation_state_based_dist(predicted_params[i, grep("prob_dist.", colnames(predicted_params))],
predicted_params[i, grep("quantiles.", colnames(predicted_params))])
}
}))
return(list("predicted_quantiles" = cbind(expected, pi_up), "predicted_params" = predicted_params))
})
#' @return A list containing all numeric parameter informations.
#' @rdname get_param_slots
#' @export
setMethod("get_param_slots",
signature(object = "markov_sim"),
function(object) {
numeric_lst <- methods::callNextMethod(object)
numeric_lst[["state_num"]] <- methods::slot(object, "state_num")
return(numeric_lst)
})
#' @return A list containing all character parameter informations.
#' @rdname get_characteristic_slots
#' @export
setMethod("get_characteristic_slots",
signature(object = "markov_sim"),
function(object) {
character_lst <- methods::callNextMethod(object)
character_lst[["cluster_type"]] <- methods::slot(object, "cluster_type")
return(character_lst)
})
#' @return A list containing all character parameter informations.
#' @rdname get_hidden_slots
#' @export
setMethod("get_hidden_slots",
signature(object = "markov_sim"),
function(object) {
hidden_lst <- methods::callNextMethod(object)
hidden_lst[["window_size_for_reg"]] <- methods::slot(object, "window_size_for_reg")
hidden_lst[["window_type_for_reg"]] <- methods::slot(object, "window_type_for_reg")
return(hidden_lst)
})
#' @export
setAs("data.frame", "markov_sim",
function(from) {
object <- methods::new("markov_sim")
for (i in names(from)) {
if (i %in% methods::slotNames(object)) {
if (methods::is(from[, i], "character")) {
if (length(strsplit(from[, i], ",")[[1]]) == 1) {
methods::slot(object, i) <- from[, i]
} else {
methods::slot(object, i) <- as.numeric(strsplit(from[, i], ",")[[1]])
}
} else {
methods::slot(object, i) <- from[, i]
}
}
}
return(object)
})
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.